Ekin, Cansu ÇiğdemSabamehr,M.Ekin,C.C.Computer Engineering2024-07-052024-07-0520230979-835031803-610.1109/IISEC59749.2023.103910082-s2.0-85184666363https://doi.org/10.1109/IISEC59749.2023.10391008https://hdl.handle.net/20.500.14411/4145Nowadays, drought is one of the trending topics in the world that has turned into a challenge for the world. By developing countries and cities worldwide, especially in the economic aspect, governments started to damage the environment such as through the use of fossil fuels, pollution of the seas, unregulated use of fresh water also deforestation for personal purposes. The presented research aims to change the format of drought mitigation strategies from traditional ways into the up to date treats. Leveraging AI technologies, including machine learning algorithms and data analytics, a comprehensive AI-driven drought management system is designed and implemented. In this system, inconsistent data have been obtained from the Ministry of Agriculture and Forestry organization and transformed into insightful data and analyzed in real-Time style to provide the status of agricultural products in Turkey. This research contributes to the fields of environmental science and agriculture by innovatively augmenting traditional approaches with AI-driven solutions. Ultimately, our research offers a means to monitor weather conditions in different regions of Turkey, moving beyond manual drought prediction and guesswork that were prevalent in previous systems. Additionally, it facilitates the evaluation of vegetation health by considering precipitation and temperature averages in each area. © 2023 IEEE.eninfo:eu-repo/semantics/closedAccessArtificial intelligenceDrought managementDrought predictionMachine learningStandardized Precipitation Index (SPI)Temperature averageTime seriesConference Object